1. Field of the Invention
The present invention relates to a demand forecasting system that forecasts a demand for IT resources in a data center, a demand forecasting method and a recording medium with a demand forecasting program recorded thereon.
2. Description of Related Art
Information Technology (IT) systems configuring economical and social infrastructures are required to have stability, robustness and cost effectiveness. IT systems have advanced increasingly in recent years, and in order to allow the functions of such IT systems to be kept and follow a change in business environment rapidly while maintaining their stability and robustness, an autonomous processing technology of the system has become essential. The autonomous processing refers to letting the system perform a part of judgment that human has made. For instance, when a load on the system increases or a failure occurs, the autonomous processing allows the IT system to change its configuration autonomously for recovery. Factors behind the increase in load on the system and the failure may include a change in business environment. The autonomous processing technology is for enhancing adaptability of the system so as to follow such a change in business environment.
For instance, in a data center, autonomous control is required for the optimum utilization of IT resources. The data center is a facility for keeping IT resources such as customer's servers, storages and networks therein and for providing circuits for connection with the Internet and maintenance and operation services, for example. Particularly, a data center capable of increasing and decreasing IT resources allocated to each customer in accordance with demands, i.e., in an on-demand manner, is called a utility type data center. In the utility type data center, it is particularly important to forecast a demand for IT resources appropriately and improve a utilization efficiency of the IT resources based on this demand forecasting.
Demands for IT resources may vary not only on a long-term basis such as a seasonal variation, but also with an event of business such as promotions. For instance, after a promotion conducted by the customer company, accesses to the Web server will increase sharply in number and then will decrease gradually.
In order for a data center to satisfy varying requirements for IT resources from a plurality of customers so as to minimize the loss in business chance of the customers and maximize the utilization efficiency of the IT resources in the data center, demand forecasting technologies are required. That is, middle-term variations resulting from a business event such as a promotion as well as long-term variations such as a seasonal variation should be forecasted.
As technologies for forecasting future demands from past usage record, many methods are available, such as a method using multiple regression analysis and a method using autoregressive model such as ARIMA.
Demand forecasting products also are available that enable demand forecasting with consideration given to effects of an event such as a promotion and enable dynamic correction of the forecasting in accordance with actual observation data by changing forecasting models and parameters (e.g., ForecastPRO® produced by Business Forecast Systems, Inc. U.S. (See citation from webpages of ForecastPRO (online) by Business Forecast Systems, Inc. as of Feb. 22, 2005, URL: http://www.forecastpro.com/) (hereinafter called document 1)).
As a known forecasting method directed to the field of marketing, JP2002-259672 A (hereinafter called patent document) discloses a method of classifying behavior patterns of users into a plurality of behavior classes, for example. According to this method, influences of an event on behavior classes of the users are calculated, whereby a variation in demand for each behavior class can be determined, and the entire demand can be forecasted by summing the thus determined variations in demand of the respective behavior classes.
The demand forecasting according to the above document 1 or the patent document, however, is for forecasting a behavior of choice between two alternatives of “buying/not buying”. On the other hand, in the demand forecasting in a data center, a continuous load generated from a series of behavior performed by a user after visiting to a website should be forecasted.
Accesses of users to IT services provided by the IT resources in a data center cannot be represented as a single event, but involve a series of spread over time and a variation in load. For instance, in the case of a user who accesses to a site for purchasing merchandise, the user visits to a website and refers to some pages for procedures of purchasing so as to achieve the final purpose of purchasing merchandise or the like. During this process, a continuous load occurs on the IT resources. The behavior by a user after he/she visits the site varies with his/her purpose. Different behavior patterns by users at the site would result in different loads generated. For instance, some users visit the site only for browsing, and other users perform downloading while browsing. In such a case, a load on the resources will be larger in the latter case.
For that reason, the load in the data center cannot be forecasted accurately with simple forecasting of the number of accesses. That is to say, even when the forecasting methods of document 1 and the patent document are applied to the usage forecasting of a website, the number of visiting a top page thereof can be forecasted simply, and a load on a server group by each user's behavior cannot be forecasted.
In this way, already-existing demand forecasting technologies are for forecasting a demand as a single event, and no consideration is given to behavior patterns of users after visiting the site. Therefore, a load cannot be forecasted accurately, thus resulting in a failure of appropriate placement planning of IT resources.
Another demand forecasting technology is disclosed in Naoki UTSUNOMIYA, Nobutoshi SAGAWA, Toshiaki TARUI and Hiroyuki KUMAZAKI, “Hitachi's Approach for Realizing the Harmonious Computing Concept in the Future”, the HITACHI HYORON, the June issue in 2004 published on Jun. 1, 2004, pp 51-54, for example (hereinafter called document 2). In this document 2, regarding a ticket sales service, a time period of the occurrence of an event and a variation in amount of demands for services are directly associated with each other for carrying out middle and long-term forecasting. However, even in the forecasting method described in document 2, accesses to IT services by users are handled as a single event. That is, a series of behavior by a user after visiting the website is not considered, and a rough estimate such as 8 times a usual one is carried out simply during an event.
Meanwhile, a method of keeping track as to how a user moves among webpages is proposed using a log of a website so as to calculate a moving probability among pages and classifying users based on such probability (for example, see Daniel A Menascé et al. “A Methodology for Workload Characterization of E-Commerce Site”, ACM E-COMMERCE 99, U.S. ACM, 1999, pp 119-128 (Hereinafter called document 3)). Further, a method of simulating a load on a server using the access number based on Queuing Network is proposed (for example, see Daniel A. Menascé, Virgilio A. F. Almeida “Capacity Planning for Web Services”, U.S. Prentice Hall, 2002 (hereinafter called document 4)). Apparently it may be considered foreseeable as to how much load is generated by a variation in demand due to an event by combining the techniques described documents 3 and 4 with the technique of the patent document. That is, it may be considered foreseeable as to how much load is generated due to an event as follows: (1) classifying users into a plurality of classes based on user's behavior by the technique described in document 3 or the patent document; (2) forecasting the access number for each class during the occurrence of the event by the technique described in the patent document; and (3) simulating a load on a server using the forecasted access number by the method described in document 4.
However, the classification of user's behavior by document 3 is for modeling a user's page moving pattern after the user visits a site firstly. And this model does not include a distribution tendency of users including some users accessing immediately after the event and other users gathering later after the spread of information by word of mouth, for example. Therefore, this technique cannot forecast how many days it will take from the occurrence of the event to the peak.
Further, according to the classification model described in document 3, users are classified into “occasional users”, “heavy users” and the like. However, no consideration is given to the moving among classes by users. Therefore, although many business events have the purpose of getting users to be regular users after the promotion time period, the classification model described in document 3 cannot forecast an increase in load after the users become regular users.
In this way, even with the combination of already-existing demand forecasting technologies, it becomes difficult to forecast a load accurately. That is, since a variation in load due to a promotion or the like cannot be forecasted accurately, it is difficult to estimate necessary IT resources beforehand. For that reason, it is difficult to understand a relationship between profits and investment in IT resources and a relationship between the scale of an event performed and investment in IT resources also. Moreover, it is impossible to make a resource augmentation plan and a promotion plan appropriately.